{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60ab3807-106d-43be-a457-7dc5a43810fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code computes the relative compactness of enacted and HD-drawn districts\n",
      "For now, compactness measure = inhabitant-averaged distance to district pop center / sqrt(district area\n"
     ]
    }
   ],
   "source": [
    "print(\"This code computes the relative compactness of enacted and HD-drawn districts\")\n",
    "print(\"For now, compactness measure = inhabitant-averaged distance to district pop center / sqrt(district area\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "749e86f9-248a-4204-b85f-ec68e484e20c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculating square, hex, circle compactness\n",
      "shape area = 0.78414\n",
      "shape area = 1.0\n",
      "shape area = 0.64952\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "circle nPoints, avgDist, 1/avgDist 196032 0.7522485703868997 1.32935\n",
      "square nPoints, avgDist, 1/avgDist 250000 0.7651954228070567 1.30686\n",
      "hex nPoints, avgDist, 1/avgDist 162377 0.7543870935110925 1.32558\n"
     ]
    }
   ],
   "source": [
    "print(\"calculating square, hex, circle compactness\")\n",
    "dx = 0.002\n",
    "dy = dx\n",
    "nX, nY = int(1./dx), int(1./dy)\n",
    "circlePoly = Point(0,0).buffer(1)\n",
    "\n",
    "ptXs = [0,1,1,0]  #iterate on these until they look good\n",
    "ptYs =  [0,0,1,1]\n",
    "pts = [Point(ptXs[i], ptYs[i]) for i in range(len(ptXs)) ]\n",
    "squarePoly = Polygon(pts)\n",
    "\n",
    "ptXs = [0, 1, 0.5 ,0]  #iterate on these until they look good\n",
    "ptYs =  [0,0,0.5*3.**0.5, 0.5*3.**0.5]\n",
    "pts = [Point(ptXs[i], ptYs[i]) for i in range(len(ptXs)) ]\n",
    "hexPoly = Polygon(pts)\n",
    "circlePoly = circlePoly.intersection(squarePoly)\n",
    "\n",
    "areas = list()\n",
    "for polly in [circlePoly, squarePoly, hexPoly]:\n",
    "    print(\"shape area =\",r5(polly.area))\n",
    "    areas.append(polly.area)\n",
    "    plotPoly(polly)\n",
    "plt.show()\n",
    "\n",
    "avgDist = list()\n",
    "names = [\"circle\", \"square\", \"hex\"]\n",
    "for ijk,polly in enumerate([circlePoly, squarePoly, hexPoly]):\n",
    "    totalDist, nPoints = 0, 0\n",
    "    for i, x in enumerate( [(k+0.5)*dx for k in range(nX)]):\n",
    "        for j, y in enumerate( [(k+0.5)*dy for k in range(nY)]):\n",
    "            pt = Point(x,y)\n",
    "            if polly.contains(pt):\n",
    "                nPoints +=1\n",
    "                totalDist += pt.distance(Point(0,0))\n",
    "                #plotPoly(pt.buffer(0.1*dx))\n",
    "    avgDist.append(totalDist/float(nPoints)/  (areas[ijk]**0.5) )\n",
    "    print(names[ijk],\"nPoints, avgDist, 1/avgDist\",nPoints, avgDist[ijk], r5(1./avgDist[ijk]))    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c42fd45d-3f7a-4b91-b8cf-b79407078c1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I would have estimated hex, circle noncompactness (avg dist to center / sqrt(area) as 0.7698 0.75\n",
      "I would have estimated hex, circle compactness (avg dist to center / sqrt(area) as 1.29904 1.33333\n"
     ]
    }
   ],
   "source": [
    "hexComp = 4. / 3.**1.5\n",
    "circleComp = 3. / 4\n",
    "print(\"I would have estimated hex, circle noncompactness (avg dist to center / sqrt(area) as\",\n",
    "      r5(hexComp),r5(circleComp) )\n",
    "print(\"I would have estimated hex, circle compactness (avg dist to center / sqrt(area) as\",\n",
    "      r5(1./hexComp),r5(1./circleComp) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ae9b88-ba8c-4b02-9539-e2a162a640a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#modify below to pull in from HD file; don't need pl2020 file.  also, define as 1/ sum(1/comp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5b1c3b2b-4016-49c4-a24d-b2068aefdeaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the two-letter state code; e.g. MN CO\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, I'll read in the census vtd shapefile\n",
      "Enter 1 below to use co_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "computing a map centroid for scaling latitude to longitude ...\n",
      "we will scale longitudinal distance by 0.77215\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"enter the two-letter state code; e.g. MN\") )\n",
    "print(\"Now, I'll read in the census vtd shapefile\")\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "if STATE == \"CA\":  #CA building blocks were tracts, as vtds were not available:\n",
    "    popFilename = STATE.lower()+\"_pl2020_t.dbf\"\n",
    "print(\"Enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"./state_map_files/\"+popFilename)\n",
    "tractGeom = tractPopFile['geometry']\n",
    "tractCP = [geo.centroid for geo in tractGeom]  #I use \"tracts\" for vtds\n",
    "tractArea = [geo.area for geo in tractGeom]\n",
    "nTracts = len(tractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "print(\"computing a map centroid for scaling latitude to longitude ...\")\n",
    "MAP = tractCP[0]\n",
    "for cp in tractCP:\n",
    "    MAP = MAP.union(cp)\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4a2aabb7-6431-4be6-80cc-d183d3bb92da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, let me read in the enacted shapefile for CO\n",
      "there are 8 enacted Districts for CO with avg district Pop 721714.25\n"
     ]
    }
   ],
   "source": [
    "year = \"2021\"  #2021 or #2022; enacted year varies by state\n",
    "print(\"OK, let me read in the enacted shapefile for\",STATE)\n",
    "enactedFile = gpd.read_file(\"enacted_map_files/\"+STATE+\"_CD_\"+year+\".dbf\")  #.dbf\n",
    "enactedFile.head()\n",
    "#enactedFile.crs = \"EPSG:4326\"\n",
    "enactedFile = enactedFile.to_crs(tractPopFile.crs)  #some states' map file may be in wrong CRS\n",
    "enactedGeom = enactedFile['geometry']\n",
    "#enactedGeom.crs = \"EPSG:4326\"\n",
    "#enactedGeom = enactedGeom.to_crs(tractGeom[0])\n",
    "nDistricts = len(enactedGeom)\n",
    "avgDistrictPop = np.sum(tractPop)/nDistricts\n",
    "print(\"there are {0} enacted Districts for {1} with avg district Pop {2}\".format(nDistricts, STATE,r3(avgDistrictPop)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1fe73b23-07e2-4776-9db6-29c16326bc37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a histogram of district area / avg\n"
     ]
    },
    {
     "data": {
      "image/png": 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PXbaj2djpFTd/Sw0AALSuM/5+81tRAADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYtoPNnj17NGPGDEVGRsrhcKigoOCmxxQXF+uBBx6Q0+nU8OHDlZ+f36wmNzdXQ4cOVXBwsBISEnTw4EG7SwMAAD2c7WBTV1enuLg45ebmtqm+srJS06dP15QpU1RRUaElS5Zo/vz52rVrl69m8+bNysjIUHZ2tsrLyxUXF6ekpCRduHDB7vIAAEAP5rAsy2r3wQ6Htm/frpSUlFZrfvGLX2jHjh06evSob2z27Nm6cuWKCgsLJUkJCQl68MEH9cYbb0iSmpqaFBUVpX/7t3/TsmXLbroOr9crl8ulmpoahYSEtLedVg1dtqPZ2OkV0zv8eQAA6Ek64+93p99jU1paKrfb7TeWlJSk0tJSSVJDQ4PKysr8agICAuR2u30116uvr5fX6/XbAAAAenX2E3g8HoWFhfmNhYWFyev16i9/+Yu+/vprNTY2tljzxRdftDhnTk6Oli9f3mlrbouWruJcz4SrOt3hatX1a7zd1gd0tO7wukT31p3/jXXLT0VlZmaqpqbGt509e/ZWLwkAANwGOv2KTXh4uKqrq/3GqqurFRISot69eyswMFCBgYEt1oSHh7c4p9PplNPp7LQ1AwCA7qnTr9gkJiaqqKjIb2z37t1KTEyUJAUFBWncuHF+NU1NTSoqKvLVAAAAtIXtYFNbW6uKigpVVFRI+uvHuSsqKnTmzBlJf32baO7cub76n/70p/rzn/+spUuX6osvvtB//ud/asuWLfrZz37mq8nIyNDbb7+tDRs26NixY1q4cKHq6uqUlpb2A9sDAAA9ie23og4dOqQpU6b4HmdkZEiSUlNTlZ+fr6qqKl/IkaTo6Gjt2LFDP/vZz7RmzRoNHjxY77zzjpKSknw1s2bN0sWLF5WVlSWPx6P4+HgVFhY2u6EYAADgRmwHm8mTJ+tGX33T0rcKT548WYcPH77hvOnp6UpPT7e7HAAAAJ9u+akoAACAlhBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjtCvY5ObmaujQoQoODlZCQoIOHjzYau3kyZPlcDiabdOnT/fVPPXUU832T5s2rT1LAwAAPVgvuwds3rxZGRkZysvLU0JCglavXq2kpCQdP35coaGhzeo/+OADNTQ0+B5fvnxZcXFxeuyxx/zqpk2bpnfffdf32Ol02l0aAADo4WxfsVm5cqUWLFigtLQ0jRo1Snl5eerTp4/Wr1/fYv2AAQMUHh7u23bv3q0+ffo0CzZOp9Ovrn///u3rCAAA9Fi2gk1DQ4PKysrkdru/nyAgQG63W6WlpW2aY926dZo9e7buvPNOv/Hi4mKFhoYqJiZGCxcu1OXLl1udo76+Xl6v128DAACwFWwuXbqkxsZGhYWF+Y2HhYXJ4/Hc9PiDBw/q6NGjmj9/vt/4tGnTtHHjRhUVFemVV15RSUmJkpOT1djY2OI8OTk5crlcvi0qKspOGwAAwFC277H5IdatW6cxY8ZowoQJfuOzZ8/2/fcxY8YoNjZW9957r4qLi/XII480myczM1MZGRm+x16vl3ADAADsXbEZOHCgAgMDVV1d7TdeXV2t8PDwGx5bV1enTZs2ad68eTd9nmHDhmngwIE6efJki/udTqdCQkL8NgAAAFvBJigoSOPGjVNRUZFvrKmpSUVFRUpMTLzhsVu3blV9fb3+6Z/+6abPc+7cOV2+fFkRERF2lgcAAHo425+KysjI0Ntvv60NGzbo2LFjWrhwoerq6pSWliZJmjt3rjIzM5sdt27dOqWkpOiuu+7yG6+trdWzzz6r/fv36/Tp0yoqKtLMmTM1fPhwJSUltbMtAADQE9m+x2bWrFm6ePGisrKy5PF4FB8fr8LCQt8NxWfOnFFAgH9eOn78uPbu3auPP/642XyBgYH67LPPtGHDBl25ckWRkZGaOnWqXnrpJb7LBgAA2NKum4fT09OVnp7e4r7i4uJmYzExMbIsq8X63r17a9euXe1ZBgAAgB9+KwoAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGKNdwSY3N1dDhw5VcHCwEhISdPDgwVZr8/Pz5XA4/Lbg4GC/GsuylJWVpYiICPXu3Vtut1snTpxoz9IAAEAPZjvYbN68WRkZGcrOzlZ5ebni4uKUlJSkCxcutHpMSEiIqqqqfNuXX37pt//VV1/V66+/rry8PB04cEB33nmnkpKS9O2339rvCAAA9Fi2g83KlSu1YMECpaWladSoUcrLy1OfPn20fv36Vo9xOBwKDw/3bWFhYb59lmVp9erVeu655zRz5kzFxsZq48aNOn/+vAoKCtrVFAAA6JlsBZuGhgaVlZXJ7XZ/P0FAgNxut0pLS1s9rra2VkOGDFFUVJRmzpypzz//3LevsrJSHo/Hb06Xy6WEhIRW56yvr5fX6/XbAAAAbAWbS5cuqbGx0e+KiySFhYXJ4/G0eExMTIzWr1+vDz/8UL/97W/V1NSkiRMn6ty5c5LkO87OnDk5OXK5XL4tKirKThsAAMBQnf6pqMTERM2dO1fx8fGaNGmSPvjgAw0aNEhvvvlmu+fMzMxUTU2Nbzt79mwHrhgAAHRXtoLNwIEDFRgYqOrqar/x6upqhYeHt2mOO+64Q2PHjtXJkyclyXecnTmdTqdCQkL8NgAAAFvBJigoSOPGjVNRUZFvrKmpSUVFRUpMTGzTHI2NjTpy5IgiIiIkSdHR0QoPD/eb0+v16sCBA22eEwAAQJJ62T0gIyNDqampGj9+vCZMmKDVq1errq5OaWlpkqS5c+fq7rvvVk5OjiTpxRdf1I9//GMNHz5cV65c0a9+9St9+eWXmj9/vqS/fmJqyZIlevnllzVixAhFR0fr+eefV2RkpFJSUjquUwAAYDzbwWbWrFm6ePGisrKy5PF4FB8fr8LCQt/Nv2fOnFFAwPcXgr7++mstWLBAHo9H/fv317hx47Rv3z6NGjXKV7N06VLV1dXp6aef1pUrV/TQQw+psLCw2Rf5AQAA3IjtYCNJ6enpSk9Pb3FfcXGx3+NVq1Zp1apVN5zP4XDoxRdf1Isvvtie5QAAAEjit6IAAIBBCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDHaFWxyc3M1dOhQBQcHKyEhQQcPHmy19u2339bDDz+s/v37q3///nK73c3qn3rqKTkcDr9t2rRp7VkaAADowWwHm82bNysjI0PZ2dkqLy9XXFyckpKSdOHChRbri4uLNWfOHP3hD39QaWmpoqKiNHXqVH311Vd+ddOmTVNVVZVve//999vXEQAA6LFsB5uVK1dqwYIFSktL06hRo5SXl6c+ffpo/fr1Ldb/13/9l/71X/9V8fHxGjlypN555x01NTWpqKjIr87pdCo8PNy39e/fv30dAQCAHstWsGloaFBZWZncbvf3EwQEyO12q7S0tE1zfPPNN/ruu+80YMAAv/Hi4mKFhoYqJiZGCxcu1OXLl1udo76+Xl6v128DAACwFWwuXbqkxsZGhYWF+Y2HhYXJ4/G0aY5f/OIXioyM9AtH06ZN08aNG1VUVKRXXnlFJSUlSk5OVmNjY4tz5OTkyOVy+baoqCg7bQAAAEP16sonW7FihTZt2qTi4mIFBwf7xmfPnu3772PGjFFsbKzuvfdeFRcX65FHHmk2T2ZmpjIyMnyPvV4v4QYAANi7YjNw4EAFBgaqurrab7y6ulrh4eE3PPa1117TihUr9PHHHys2NvaGtcOGDdPAgQN18uTJFvc7nU6FhIT4bQAAALaCTVBQkMaNG+d34++1G4ETExNbPe7VV1/VSy+9pMLCQo0fP/6mz3Pu3DldvnxZERERdpYHAAB6ONufisrIyNDbb7+tDRs26NixY1q4cKHq6uqUlpYmSZo7d64yMzN99a+88oqef/55rV+/XkOHDpXH45HH41Ftba0kqba2Vs8++6z279+v06dPq6ioSDNnztTw4cOVlJTUQW0CAICewPY9NrNmzdLFixeVlZUlj8ej+Ph4FRYW+m4oPnPmjAICvs9La9euVUNDg/7xH//Rb57s7Gy98MILCgwM1GeffaYNGzboypUrioyM1NSpU/XSSy/J6XT+wPYAAEBP0q6bh9PT05Went7ivuLiYr/Hp0+fvuFcvXv31q5du9qzDAAAAD/8VhQAADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMEa7gk1ubq6GDh2q4OBgJSQk6ODBgzes37p1q0aOHKng4GCNGTNGO3fu9NtvWZaysrIUERGh3r17y+1268SJE+1ZGgAA6MFsB5vNmzcrIyND2dnZKi8vV1xcnJKSknThwoUW6/ft26c5c+Zo3rx5Onz4sFJSUpSSkqKjR4/6al599VW9/vrrysvL04EDB3TnnXcqKSlJ3377bfs7AwAAPY7tYLNy5UotWLBAaWlpGjVqlPLy8tSnTx+tX7++xfo1a9Zo2rRpevbZZ/WjH/1IL730kh544AG98cYbkv56tWb16tV67rnnNHPmTMXGxmrjxo06f/68CgoKflBzAACgZ+llp7ihoUFlZWXKzMz0jQUEBMjtdqu0tLTFY0pLS5WRkeE3lpSU5AstlZWV8ng8crvdvv0ul0sJCQkqLS3V7Nmzm81ZX1+v+vp63+OamhpJktfrtdNOmzXVf9Ou4zprPV2ppd5vt76uX+Pttj6go3WH1yW6t676N3ZtTsuyOmxOW8Hm0qVLamxsVFhYmN94WFiYvvjiixaP8Xg8LdZ7PB7f/mtjrdVcLycnR8uXL282HhUV1bZGuohr9a1eQee43fu63dcHdAb+3aOzdea/satXr8rlcnXIXLaCze0iMzPT7ypQU1OT/vd//1d33XWXHA5Hhz2P1+tVVFSUzp49q5CQkA6b93ZEr2aiV3P1pH7p1UzXev3Tn/6kyMjIDpvXVrAZOHCgAgMDVV1d7TdeXV2t8PDwFo8JDw+/Yf21/6yurlZERIRfTXx8fItzOp1OOZ1Ov7F+/frZacWWkJAQ4/+BXUOvZqJXc/WkfunVTHfffbcCAjru22dszRQUFKRx48apqKjIN9bU1KSioiIlJia2eExiYqJfvSTt3r3bVx8dHa3w8HC/Gq/XqwMHDrQ6JwAAQEtsvxWVkZGh1NRUjR8/XhMmTNDq1atVV1entLQ0SdLcuXN19913KycnR5K0ePFiTZo0Sb/+9a81ffp0bdq0SYcOHdJbb70lSXI4HFqyZIlefvlljRgxQtHR0Xr++ecVGRmplJSUjusUAAAYz3awmTVrli5evKisrCx5PB7Fx8ersLDQd/PvmTNn/C4pTZw4Ue+9956ee+45/fKXv9SIESNUUFCg+++/31ezdOlS1dXV6emnn9aVK1f00EMPqbCwUMHBwR3QYvs5nU5lZ2c3e9vLRPRqJno1V0/ql17N1Fm9OqyO/IwVAADALcRvRQEAAGMQbAAAgDEINgAAwBgEGwAAYIweH2xyc3M1dOhQBQcHKyEhQQcPHrxh/datWzVy5EgFBwdrzJgx2rlzZxet9Iez02t+fr4cDoffdqs/pdZWe/bs0YwZMxQZGSmHw9GmH1MtLi7WAw88IKfTqeHDhys/P7/T19kR7PZaXFzc7Lw6HI5Wf77kdpGTk6MHH3xQffv2VWhoqFJSUnT8+PGbHtddX6/t6be7vmbXrl2r2NhY3xfSJSYm6ne/+90Nj+mu59Vur931nLZkxYoVvq93uZGOOLc9Oths3rxZGRkZys7OVnl5ueLi4pSUlKQLFy60WL9v3z7NmTNH8+bN0+HDh5WSkqKUlBQdPXq0i1dun91epb9+82VVVZVv+/LLL7twxe1XV1enuLg45ebmtqm+srJS06dP15QpU1RRUaElS5Zo/vz52rVrVyev9Iez2+s1x48f9zu3oaGhnbTCjlFSUqJFixZp//792r17t7777jtNnTpVdXV1rR7TnV+v7elX6p6v2cGDB2vFihUqKyvToUOH9Hd/93eaOXOmPv/88xbru/N5tdur1D3P6fX++Mc/6s0331RsbOwN6zrs3Fo92IQJE6xFixb5Hjc2NlqRkZFWTk5Oi/WPP/64NX36dL+xhIQE61/+5V86dZ0dwW6v7777ruVyubpodZ1HkrV9+/Yb1ixdutQaPXq039isWbOspKSkTlxZx2tLr3/4wx8sSdbXX3/dJWvqLBcuXLAkWSUlJa3WdOfX6/Xa0q8pr1nLsqz+/ftb77zzTov7TDqvlnXjXk04p1evXrVGjBhh7d6925o0aZK1ePHiVms76tz22Cs2DQ0NKisrk9vt9o0FBATI7XartLS0xWNKS0v96iUpKSmp1frbRXt6laTa2loNGTJEUVFRN/1/Fd1Zdz2vP0R8fLwiIiL06KOP6tNPP73Vy7GtpqZGkjRgwIBWa0w6r23pV+r+r9nGxkZt2rRJdXV1rf6kjinntS29St3/nC5atEjTp09vds5a0lHntscGm0uXLqmxsdH3jcnXhIWFtXq/gcfjsVV/u2hPrzExMVq/fr0+/PBD/fa3v1VTU5MmTpyoc+fOdcWSu1Rr59Xr9eovf/nLLVpV54iIiFBeXp62bdumbdu2KSoqSpMnT1Z5efmtXlqbNTU1acmSJfrJT37i9w3m1+uur9frtbXf7vyaPXLkiP7mb/5GTqdTP/3pT7V9+3aNGjWqxdrufl7t9Nqdz6kkbdq0SeXl5b6fWLqZjjq3tn9SAT1DYmKi3/+LmDhxon70ox/pzTff1EsvvXQLV4YfIiYmRjExMb7HEydO1KlTp7Rq1Sr95je/uYUra7tFixbp6NGj2rt3761eSpdoa7/d+TUbExOjiooK1dTU6L//+7+VmpqqkpKSVv/gd2d2eu3O5/Ts2bNavHixdu/e3eU3PPfYYDNw4EAFBgaqurrab7y6ulrh4eEtHhMeHm6r/nbRnl6vd8cdd2js2LE6efJkZyzxlmrtvIaEhKh37963aFVdZ8KECd0mJKSnp+ujjz7Snj17NHjw4BvWdtfX6//PTr/X606v2aCgIA0fPlySNG7cOP3xj3/UmjVr9Oabbzar7e7n1U6v1+tO57SsrEwXLlzQAw884BtrbGzUnj179MYbb6i+vl6BgYF+x3TUue2xb0UFBQVp3LhxKioq8o01NTWpqKio1fc7ExMT/eolaffu3Td8f/R20J5er9fY2KgjR44oIiKis5Z5y3TX89pRKioqbvvzalmW0tPTtX37dn3yySeKjo6+6THd+by2p9/rdefXbFNTk+rr61vc153Pa0tu1Ov1utM5feSRR3TkyBFVVFT4tvHjx+uJJ55QRUVFs1AjdeC5tX+Pszk2bdpkOZ1OKz8/3/rTn/5kPf3001a/fv0sj8djWZZlPfnkk9ayZct89Z9++qnVq1cv67XXXrOOHTtmZWdnW3fccYd15MiRW9VCm9ntdfny5dauXbusU6dOWWVlZdbs2bOt4OBg6/PPP79VLbTZ1atXrcOHD1uHDx+2JFkrV660Dh8+bH355ZeWZVnWsmXLrCeffNJX/+c//9nq06eP9eyzz1rHjh2zcnNzrcDAQKuwsPBWtdBmdntdtWqVVVBQYJ04ccI6cuSItXjxYisgIMD6/e9/f6taaJOFCxdaLpfLKi4utqqqqnzbN99846sx6fXann6762t22bJlVklJiVVZWWl99tln1rJlyyyHw2F9/PHHlmWZdV7t9tpdz2lrrv9UVGed2x4dbCzLsv7jP/7Duueee6ygoCBrwoQJ1v79+337Jk2aZKWmpvrVb9myxbrvvvusoKAga/To0daOHTu6eMXtZ6fXJUuW+GrDwsKsv//7v7fKy8tvwartu/aR5uu3a/2lpqZakyZNanZMfHy8FRQUZA0bNsx69913u3zd7WG311deecW69957reDgYGvAgAHW5MmTrU8++eTWLN6GlnqU5HeeTHq9tqff7vqa/ed//mdryJAhVlBQkDVo0CDrkUce8f2htyyzzqvdXrvrOW3N9cGms86tw7Isy941HgAAgNtTj73HBgAAmIdgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABj/D9jRN4ojr4RFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, geo in enumerate(enactedGeom):\n",
    "    plotPoly(geo)\n",
    "    plotCenter(i+1,geo)\n",
    "plt.show()\n",
    "CDrelArea = [geo.area for geo in enactedGeom]\n",
    "stateArea = np.sum(CDrelArea)\n",
    "CDrelArea = [CDrelArea[i] * nDistricts / np.sum(CDrelArea) for i in range(nDistricts) ]\n",
    "print(\"here is a histogram of district area / avg\")\n",
    "\n",
    "plt.hist(CDrelArea,bins=100)\n",
    "#plt.hist(CDrelArea,bins=[0, 0.01,0.02,0.04,0.1,0.2,0.5,1,2,4,nDistricts])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b4045a23-4040-49c9-a3bb-ed50a9313c3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "assign VTDs to each ENACTED district\n"
     ]
    }
   ],
   "source": [
    "print(\"assign VTDs to each ENACTED district\")\n",
    "enactedVTDlist = [list() for geo in enactedGeom]\n",
    "for v in range(nTracts):\n",
    "    for i, geo in enumerate(enactedGeom):\n",
    "        if geo.contains(tractCP[v]):\n",
    "            enactedVTDlist[i].append(v)\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "37ff3335-2f9f-4a1b-81cc-2eb12b24cf15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for CO\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. MN4110opt3patchedVTDs.csv CO3108contigPatchEvenMore.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble set describes 3108 drawn districts\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE) #these districts are in Census VTD order\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. MN4110opt3patchedVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nDrawnDistricts, nHDs = nRows, nRows  #nomenclature\n",
    "print(\"This ensemble set describes\",nRows,\"drawn districts\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDwt = HDdf['HDweight']  #'HDwt'\n",
    "HDweight = HDwt.copy()  #nomenclature\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf['HDvtdList']\n",
    "HDvtdList = [ast.literal_eval(L) for L in HDvtdListString]  #ignore partials for compactness\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDwt)) )\n",
    "if abs(np.sum(HDwt) - 1) > 0.001:\n",
    "    shouldWeCorrect = input(\"enter 1 to renormalize the ensemble weight to 1, otherwise enter 0\")\n",
    "    if shouldWeCorrect == 1:\n",
    "        factor = np.sum(HDweight)\n",
    "        HDwt = [current / factor for current in HDwt]\n",
    "        HDweight = HDwt.copy()  #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7016d5bd-d320-4fe7-b3ea-59cc8043f928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We define compactness as the inhabitant-avg'd distance to pop centroid, ...\n",
      " ... normalized by sqrt(avg district area). We save the 'sprawl' = 1/compactness of each district\n",
      "Here are the enacted pop centerpts\n",
      "enacted district 1 has area/avg of only 0.01208 so we approximate as octagon\n",
      "enacted district 6 's CP was outside its district.  Reassigning to POINT (-104.866736110424 39.64785893968258)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and now the HD-drawn compactness scores\n",
      "working on compactness for HD 0 out of 3108\n",
      "working on compactness for HD 500 out of 3108\n",
      "working on compactness for HD 1000 out of 3108\n",
      "working on compactness for HD 1500 out of 3108\n",
      "working on compactness for HD 2000 out of 3108\n",
      "working on compactness for HD 2500 out of 3108\n",
      "working on compactness for HD 3000 out of 3108\n",
      "here are the centerpoints of HD districts with sprawl < 0.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We define compactness as the inhabitant-avg'd distance to pop centroid, ...\")\n",
    "print(\" ... normalized by sqrt(avg district area). We save the 'sprawl' = 1/compactness of each district\")\n",
    "print(\"Here are the enacted pop centerpts\")\n",
    "stateArea = np.sum([geo.area for geo in enactedGeom])\n",
    "avgDistrictArea = np.average([geo.area for geo in enactedGeom])\n",
    "minAreaRatio = 0.02\n",
    "minArea = minAreaRatio * stateArea / float(nDistricts)\n",
    "avgDistrictL = (xScale * avgDistrictArea) ** 0.5\n",
    "enactedSprawl, HDsprawl = [0.]*nDistricts, [0.]*nHDs  #dimensionless inhab-center distance relative to avg district span\n",
    "isTinyEnacted, isTinyHD = [0.]*nDistricts, [0.]*nHDs\n",
    "for i,L in enumerate(enactedVTDlist) :\n",
    "    if CDrelArea[i] > minAreaRatio:\n",
    "        districtCP = getHDcp(tractCP, tractPop, [v for v in L] )\n",
    "        if enactedGeom[i].disjoint(districtCP):\n",
    "            districtCP = nearest_points(enactedGeom[i],districtCP)[0]\n",
    "            print(\"enacted district\",i+1,\"'s CP was outside its district.  Reassigning to\",districtCP)\n",
    "        plotPoly(enactedGeom[i])\n",
    "        plotPoly(districtCP.buffer(0.03*CDrelArea[i]**0.5))\n",
    "        dTotalDistance, dPop = 0., float( np.sum([tractPop[v] for v in L]) )\n",
    "        for v in L:\n",
    "            dTotalDistance += tractPop[v] * getLongDist(districtCP,tractCP[v],xScale)\n",
    "        enactedSprawl[i] = ( dTotalDistance / dPop ) / avgDistrictL\n",
    "    else:\n",
    "        isTinyEnacted[i] = 1\n",
    "        print(\"enacted district\",i+1,\"has area/avg of only\",r5(CDrelArea[i]),\"so we approximate as octagon\")\n",
    "        enactedSprawl[i] = 0.76 * (xScale * enactedGeom[i].area ** 0.5 ) / avgDistrictL #between hex and square for small districts\n",
    "\n",
    "plt.show()\n",
    "print(\"... and now the HD-drawn compactness scores\")\n",
    "\n",
    "for i,L in enumerate(HDvtdList) :\n",
    "    if i%500 == 0:\n",
    "        print(\"working on compactness for HD\",i,\"out of\",nHDs)\n",
    "    if len(L) > 0:  #we didn't draw HDs for unpopulated tracts\n",
    "        HDarea = np.sum([tractArea[v] for v in L])\n",
    "        if HDarea > minArea:\n",
    "            districtCP = getHDcp(tractCP, tractPop, [v for v in L] )\n",
    "            #plotPoly(HDGeom[i])\n",
    "            dArea = xScale * np.sum([tractArea[v] for v in L])\n",
    "            dTotalDistance, dPop = 0., float( np.sum([tractPop[v] for v in L]) )\n",
    "            for v in L:\n",
    "                dTotalDistance += tractPop[v] * getLongDist(districtCP,tractCP[v],xScale)\n",
    "            HDsprawl[i] = ( dTotalDistance / dPop ) / avgDistrictL\n",
    "        else:\n",
    "            isTinyHD[i] = 1\n",
    "            HDsprawl[i] = 0.76 * (xScale * HDarea ** 0.5 ) / avgDistrictL #between hex and square for small district\n",
    "\n",
    "for i in range(nDistricts):\n",
    "    plotPoly(enactedGeom[i])\n",
    "print(\"here are the centerpoints of HD districts with sprawl < 0.1\")\n",
    "for i in range(nHDs):\n",
    "    if HDsprawl[i] < 0.1 and len(HDvtdList[i]) > 0 :\n",
    "        plt.text(tractCPx[i],tractCPy[i],\"o\", fontsize = max(4, 12 + 4 * math.log(HDsprawl[i]) ) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2a5fc589-420c-48bb-9a9d-47ffb75e9d89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting these as comparative histograms (cumulative, then frequency)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CO avg compactness for enacted and HD-drawn are 3.98798 4.52657\n"
     ]
    }
   ],
   "source": [
    "print(\"plotting these as comparative histograms (cumulative, then frequency)\")\n",
    "plt.axhline(1.0,ls=\"--\")\n",
    "plt.hist(enactedSprawl, weights = [1./nDistricts for i in range(nDistricts)],histtype=\"step\",label=\"enacted\",bins=20,cumulative=True)\n",
    "plt.hist(HDsprawl,      weights = HDweight,                             histtype=\"step\",label=\"HD-drawn\",bins=20,cumulative=True)\n",
    "plt.xlabel(\"relative inhabitant-to-center distance\")\n",
    "plt.axvline(1.,ls=\"--\",label=\"avg distr L\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.hist(enactedSprawl, weights = [1./nDistricts for i in range(nDistricts)],histtype=\"step\",label=\"enacted\",bins=20)\n",
    "plt.hist(HDsprawl,      weights = HDweight,                             histtype=\"step\",label=\"HD-drawn\",bins=20)\n",
    "plt.xlabel(\"relative inhabitant-to-center distance\")\n",
    "plt.axvline(1.,ls=\"--\",label=\"avg distr L\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "enactedComp = 1./np.average(enactedSprawl)\n",
    "HDcomp = 1./ np.sum( [HDweight[t] * HDsprawl[t] for t in range(nHDs) ] )\n",
    "print(STATE,\"avg compactness for enacted and HD-drawn are\",r5(enactedComp), r5(HDcomp) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "48dae4e7-a895-4769-bf0e-e1419bd86c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "enactedColumn = [\"nDistricts\",nDistricts,\"  \",\"enactedComp\",r5(enactedComp), \"  \",\n",
    "                 \"HDcomp\",r5(HDcomp),\"  \",\"indivComp\"]\n",
    "for i in range(nDistricts):\n",
    "    enactedColumn.append(1./enactedSprawl[i])\n",
    "nToFill = nHDs - len(enactedColumn)\n",
    "for n in range(nToFill):\n",
    "    enactedColumn.append(\"-\")\n",
    "\n",
    "outDF = HDdf.copy()\n",
    "outDF.insert(2,\"enacted\",enactedColumn,True)\n",
    "outDF.insert(3,\"HDcomp\",[1./max(HDsprawl[i],0.0000001) for i in range(nHDs) ] )\n",
    "outname = STATE+\"compactness.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13b0fd97-17b2-48ce-9dc9-6c59cd8cc067",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "411f5285-128b-49a6-9d2f-8ad3d4ce9ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's read in table of state lean, fractional seat GM, enacted and HD compactness\n",
      "I read in the master data.  There are 22 states. Here is the list of map drawers ['Comsn', 'GOP', 'Dem', 'Mixed', 'Court']\n"
     ]
    }
   ],
   "source": [
    "print(\"Let's read in table of state lean, fractional seat GM, enacted and HD compactness\")\n",
    "inFile = \"./2024state_HD_output/compactSummary.csv\"\n",
    "inDF = pd.read_csv(inFile)\n",
    "stateName = inDF[\"STATE\"]\n",
    "nStates = len(stateName)\n",
    "enComp = inDF[\"enComp\"]\n",
    "HDcomp = inDF[\"HDcomp\"]\n",
    "stateGOP =   inDF[\"stateGOP\"]\n",
    "enGOPfrac = inDF[\"enGOPfrac\"]\n",
    "HDGOPfrac = inDF[\"HDGOPfrac\"]\n",
    "GMfrac = [enGOPfrac[i] - HDGOPfrac[i] for i in range(nStates)]\n",
    "isPartisan = inDF[\"isPartisan\"]  #we assign a 0 if drawn by nonpolitical commission\n",
    "agent = inDF[\"agent\"][:nStates]\n",
    "allAgents = list()\n",
    "for a in agent:\n",
    "    if a not in allAgents:\n",
    "        allAgents.append(a)\n",
    "print(\"I read in the master data.  There are\",nStates,\"states. Here is the list of map drawers\",allAgents)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f01d9c66-e86e-4fae-a25f-cd9824de0eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nAgents = len(allAgents)\n",
    "aSymbol = [\"*\",\">\",\"<\",\"o\",\"P\" ]\n",
    "aColor = [\"green\",\"red\",\"blue\",\"purple\",\"black\"]\n",
    "for i in range(nStates):\n",
    "    agentNo = allAgents.index(agent[i])\n",
    "    plt.scatter(HDcomp[i],enComp[i],marker=aSymbol[agentNo],color=aColor[agentNo])\n",
    "    xShift,yShift = 0.,0.07\n",
    "    if stateName[i] in [\"AZ\",\"CA\",\"CO\",\"IL\",\"TX\"]:\n",
    "        yShift = -0.12\n",
    "    if stateName[i] in [\"MI\",\"NJ\"]:\n",
    "        xShift += 0.05\n",
    "        yShift -= 0.02\n",
    "    if stateName[i] in [\"IL\",\"IN\",\"NY\",\"WA\",\"TX\",\"WI\"] or agent[i] == allAgents[0]:\n",
    "        plt.text(HDcomp[i]+xShift,enComp[i]+yShift,stateName[i],ha=\"center\",color=aColor[agentNo])\n",
    "    plt.plot([1.6,4.],[1.6,4.],ls=\"-\",lw=0.4,color=\"black\",dashes = (8,6))\n",
    "xStart, yStart, xDelta, yDelta = 1.5, 4., 0.1, -0.15\n",
    "for i in range(nAgents):\n",
    "    plt.scatter(xStart, yStart + yDelta*i,marker=aSymbol[i],color=aColor[i])\n",
    "    plt.text(xStart+xDelta, yStart + yDelta*i - 0.05, allAgents[i],color=aColor[i])\n",
    "plt.text(2.,2.1,\"y = x\",rotation=45)\n",
    "plt.xlabel(\"expected compactness\")\n",
    "plt.ylabel(\"enacted compactnness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bb9712d7-9579-4e94-abcd-b377751f8289",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 nan nan 5\n"
     ]
    }
   ],
   "source": [
    "print(i,stateName[i],agent[i],allAgents.index(agent[i]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c43a9e30-1080-48ef-9a0a-908f27c7e572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n"
     ]
    }
   ],
   "source": [
    "print(nStates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8040d985-baad-45f3-8bc5-09169f860fe1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
